The core of link prediction is correlation analysis, which reveals the intrinsic similarity between objects. A higher score of the link prediction metric indicates a higher probability of forming a link. Methods based on node centrality or common neighbors and their relevant variants indeed achieved good results[1]}, [2]}, [3]}. For example, Resource Allocation index (RA) [4]} considers that each node in the network has certain resources and distributes the resources equally to its neighbors. Besides, RA index shows good performance with low time complexity and high accuracy on some datasets. The formula of RA index is as follows.
\(RA(x,y) = \sum \limits _{z \in \Gamma (x) \cap \Gamma (y)}\frac{1}{|\Gamma (z)|}.\) 
